Optical network modeling and engineering are concerned with placing viable services (on wavelengths) into a network. Conventionally, link modeling and engineering is performed for forecast tolerant engineering, i.e., all wavelengths, in a Wavelength Division Multiplexed (WDM) are treated equally and any wavelength placed in the network is guaranteed to work (based on the engineering), regardless of conditions, i.e. a worst-case engineering approach. The corollary to this is that initial wavelengths in new deployments will have a large amount of excess margin or wavelengths in a fully-utilized (not all wavelengths present on all links due to contention or blocking) system have excess margin. As optical networks progress, conventional transmitters/receivers (TX/RX), which typically utilized simple on-off keying, are evolving to advanced optical Modulators/Demodulators (modems) with adaptable modulation formats. Other modems (e.g., cell phones, digital subscriber loop modems, cable modems, etc.) perform optimization to provide additional capacity based on current conditions. However, conventionally, optical networks have not performed optimization except for the initial viability determination during link modeling and engineering. Note, while the other modems listed above can perform their optimization with tradeoffs independently on multiple wavelengths (owing to a linear medium), optical networks must perform these optimizations for a full set of wavelengths due to nonlinear interactions in optical fiber and to ensure proper operation at worst case, i.e. full-fill. Stated differently, optical network optimization is vastly different from optimizing in the other modems described above. Additionally, optical networks can differentiate between wavelengths that may or may not need additional capacity (based on the underlying optical modem and service being transported) while the other modems seek to maximize capacity on their linear medium.
It is expected that optical network deployment will move away from up-front engineering for worst-case, end-of-life conditions towards automatic optimization for current conditions, a process that can continually run over the life of the deployment. This will provide additional opportunities for more bandwidth, without increasing capital costs, as optical equipment is run based on a current optimization rather than a forecast tolerant, end-of-life optimization. In this manner, it is important to determine systems and methods for hour-by-hour optimization of optical networks across 15+ years of change (or whatever time period the equipment is engineered and deployed to). This problem statement can be summarized as how to understand mechanisms to optimize all parameters available in highly nonlinear optical networks.
Accordingly, there is a need for margin-based optimization systems and methods based on the characteristics of optical networks and understanding how these can be used to maximize bandwidth based on current conditions.